SySAP: a system-level predictor of deleterious single amino acid polymorphisms.
10.1007/s13238-011-1130-2
- Author:
Tao HUANG
1
;
Chuan WANG
;
Guoqing ZHANG
;
Lu XIE
;
Yixue LI
Author Information
1. Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
- Publication Type:Journal Article
- MeSH:
Amino Acids;
genetics;
Computational Biology;
methods;
Conserved Sequence;
Databases, Protein;
Humans;
Internet;
Polymorphism, Single Nucleotide;
genetics;
Reproducibility of Results;
Software
- From:
Protein & Cell
2012;3(1):38-43
- CountryChina
- Language:English
-
Abstract:
Single amino acid polymorphisms (SAPs), also known as non-synonymous single nucleotide polymorphisms (nsSNPs), are responsible for most of human genetic diseases. Discriminate the deleterious SAPs from neutral ones can help identify the disease genes and understand the mechanism of diseases. In this work, a method of deleterious SAP prediction at system level was established. Unlike most existing methods, our method not only considers the sequence and structure information, but also the network information. The integration of network information can improve the performance of deleterious SAP prediction. To make our method available to the public, we developed SySAP (a System-level predictor of deleterious Single Amino acid Polymorphisms), an easy-to-use and high accurate web server. SySAP is freely available at http://www.biosino.org/ SySAP/ and http://lifecenter.sgst.cn/SySAP/.
